Overview
SQL Server Analysis Services (SSAS) is the technology from the Microsoft Business Intelligence stack, to develop Online Analytical Processing (OLAP) solutions. In simple terms, you can use SSAS to create cubes using data from data marts / data warehouse for deeper and faster data analysis.
Cubes are multi-dimensional data sources which have dimensions and facts (also known as measures) as its basic constituents. From a relational perspective dimensions can be thought of as master tables and facts can be thought of as measureable details. These details are generally stored in a pre-aggregated proprietary format and users can analyze huge amounts of data and slice this data by dimensions very easily. Multi-dimensional expression (MDX) is the query language used to query a cube, similar to the way T-SQL is used to query a table in SQL Server.
Simple examples of dimensions can be product / geography / time / customer, and similar simple examples of facts can be orders / sales. A typical analysis could be to analyze sales in Asia-pacific geography during the past 5 years. You can think of this data as a pivot table where geography is the column-axis and years is the row axis, and sales can be seen as the values. Geography can also have its own hierarchy like Country->City->State. Time can also have its own hierarchy like Year->Semester->Quarter. Sales could then be analyzed using any of these hierarchies for effective data analysis.
A typical higher level cube development process using SSAS involves the following steps:
1) Reading data from a dimensional model
2) Configuring a schema in BIDS (Business Intelligence Development Studio)
3) Creating dimensions, measures and cubes from this schema
4) Fine tuning the cube as per the requirements
5) Deploying the cube
In this tutorial we will step through a number of topics that you need to understand in order to successfully create a basic cube. Our high level outline is as follows:
Cubes are multi-dimensional data sources which have dimensions and facts (also known as measures) as its basic constituents. From a relational perspective dimensions can be thought of as master tables and facts can be thought of as measureable details. These details are generally stored in a pre-aggregated proprietary format and users can analyze huge amounts of data and slice this data by dimensions very easily. Multi-dimensional expression (MDX) is the query language used to query a cube, similar to the way T-SQL is used to query a table in SQL Server.
Simple examples of dimensions can be product / geography / time / customer, and similar simple examples of facts can be orders / sales. A typical analysis could be to analyze sales in Asia-pacific geography during the past 5 years. You can think of this data as a pivot table where geography is the column-axis and years is the row axis, and sales can be seen as the values. Geography can also have its own hierarchy like Country->City->State. Time can also have its own hierarchy like Year->Semester->Quarter. Sales could then be analyzed using any of these hierarchies for effective data analysis.
A typical higher level cube development process using SSAS involves the following steps:
1) Reading data from a dimensional model
2) Configuring a schema in BIDS (Business Intelligence Development Studio)
3) Creating dimensions, measures and cubes from this schema
4) Fine tuning the cube as per the requirements
5) Deploying the cube
In this tutorial we will step through a number of topics that you need to understand in order to successfully create a basic cube. Our high level outline is as follows:
- Design and develop a star-schema
- Create dimensions, hierarchies, and cubes
- Process and deploy a cube
- Develop calculated measures and named sets using MDX
- Browse the cube data using Excel as the client tool
When you start learning SSAS, you should have a reasonable relational database background. But when you start working in a multi-dimensional environment, you need to stop thinking from a two-dimensional (relational database) perspective, which will develop over time.
In this tutorial, we will also try to develop an understanding of OLAP development from the eyes of an OLTP practitioner.
Source Collected from mssqltips.com
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